A data assimilation scheme to improve groundwater state estimation in the Aqui-FR modelling platform

Abstract. Groundwater is a key resource for human activities, and anticipating its evolutions months in advance is a major challenge for stakeholders. Hydrological model for subsurface flows can be used for groundwater level forecasts. However, due to uncertainties in the model's forcings and parameters, forecast initial state estimation may be inaccurate. We propose the implementation of a sequential data assimilation (DA) scheme within the Aqui-FR modelling platform, aiming at improving groundwater state estimation over a regional scale for future seasonal forecasting system.

We assimilated in situ groundwater level observations into a regional hydrogeological model, using a Localized Ensemble Kalman Filter (LEnKF). Two localization methods are assessed to evaluate the best way to propagate data assimilation increment from observation sites into the model space. A distance based method is compared to a correlation method, based on a variogram analysis.

Both method show good performances to improve groundwater head simulations, with a root-mean-square error (RMSE) reduction of 90% compared to a reference simulation without DA [open loop (OL) run]. Experiments with validation observations sites show that the correlation method lead to a more robust DA analysis, with less degradation of the simulation compared to OL run and measurements.

Hindcast experiments using reanalysis of atmospheric forcing suggest that state assimilation, in context of inertial aquifers, can help improve forecast within a six-month range. The persistence of DA correction varies within the model space domain and may be due by an initial calibration that could be improved. After a three months lead time, 75% of assimilated observation sites still show an improvement of RMSE compared to OL.